Gartner’s Post

I believe that many of the use cases presented don't directly apply to healthcare, resulting in a skewed perception. To draw a proper conclusion, there should have been more healthcare-specific use cases included, such as drug discovery for the pharmaceutical industry. I would have added digital health (telemedicine) or patient / employee / customer experience.

The intersection of Gartner's research and Generative AI can provide valuable insights into trends, best practices, and the potential impacts of generative technologies in different industries. Gartner often analyzes the implications of emerging technologies, including AI advancements, on business strategies.

How does "content creation" rank "Very High" overall when all use cases but Text rank Low?

In my view, all these generic use case classifications add little value and are often questionable (e.g. why no uses cases in Government and Utilities?!). Actually, GenAI can support more or less every business process; in some cases merely supportive, while in others truly transformative. For this you have to take a much more specific look at the business capabilities.

I couldn't agree more with the point that there is a missed opportunity in the public sector when it comes to leveraging Generative AI for use cases, especially in areas like lawmaking and policy implementation. As I highlighted in my recent post about accelerating legislation into actionable frameworks, the potential for GenAI to support and enhance this process is immense. By utilizing Generative AI, we can prototype legislation at an early stage, transforming legal text into clear, executable business rules and service blueprints. This not only helps in identifying bottlenecks early but also enables faster, more efficient implementation of laws, ultimately improving the quality of governance. GenAI could play a pivotal role in this process, helping streamline the development and testing of new laws before they are formally adopted, reducing delays, and enhancing collaboration among stakeholders.

The low adoption of generative AI in data and analytics often comes down to practical challenges on the ground. It’s not just about the tech, it’s about figuring out how to integrate it into existing systems, navigating tight budgets, and overcoming resistance to change. I've seen how teams hesitate to trust AI-driven insights, especially when the stakes are high, and they can’t easily explain the “why” behind the recommendations. But I also know the potential is huge. Once the tools become more accessible, less resource-intensive, and easier to explain, I’m confident more industries will see the value and start adopting it. It’s all about finding the right balance between ambition and readiness.

Because of user-friendly platforms that enable nonexperts to utilise AI for business, individual duties, research, and creative endeavours, AI will continue to be incorporated into both the personal and professional realms. Like today's website builders, these platforms will let small firms, educators, and entrepreneurs create unique AI solutions without needing extensive technical knowledge. Microservices and API-driven AI will enable companies to modularly incorporate sophisticated AI features into their current systems. This method will expedite the creation of unique apps without necessitating a high level of AI knowledge.

This might be the most useless chart I’ve ever seen.

This only captures very high level use-cases in industries and is missing number of industry specific workflow use-cases.